Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements

Many studies of the human brain have explored the relationship between cortical thickness and cognition, phenotype, or disease. Due to the subjectivity and time requirements in manual measurement of cortical thickness, scientists have relied on robust software tools for automation which facilitate the testing and refinement of neuroscientific hypotheses. The most widely used tool for cortical thickness studies is the publicly available, surface-based FreeSurfer package. Critical to the adoption of such tools is a demonstration of their reproducibility, validity, and the documentation of specific implementations that are robust across large, diverse imaging datasets. To this end, we have developed the automated, volume-based Advanced Normalization Tools (ANTs) cortical thickness pipeline comprising well-vetted components such as SyGN (multivariate template construction), SyN (image registration), N4 (bias correction), Atropos (n-tissue segmentation), and DiReCT (cortical thickness estimation). In this work, we have conducted the largest evaluation of automated cortical thickness measures in publicly available data, comparing FreeSurfer and ANTs measures computed on 1205 images from four open data sets (IXI, MMRR, NKI, and OASIS), with parcellation based on the recently proposed Desikan-Killiany-Tourville (DKT) cortical labeling protocol. We found good scan-rescan repeatability with both FreeSurfer and ANTs measures. Given that such assessments of precision do not necessarily reflect accuracy or an ability to make statistical inferences, we further tested the neurobiological validity of these approaches by evaluating thickness-based prediction of age and gender. ANTs is shown to have a higher predictive performance than FreeSurfer for both of these measures. In promotion of open science, we make all of our scripts, data, and results publicly available which complements the use of open image data sets and the open source availability of the proposed ANTs cortical thickness pipeline.

[1]  Carles Falcon,et al.  Brain T1 intensity changes after levodopa administration in healthy subjects: a voxel-based morphometry study. , 2006, British journal of clinical pharmacology.

[2]  Ron Mengelers,et al.  The Effects of FreeSurfer Version, Workstation Type, and Macintosh Operating System Version on Anatomical Volume and Cortical Thickness Measurements , 2012, PloS one.

[3]  T. Cizadlo,et al.  Quantitative in vivo measurement of gyrification in the human brain: changes associated with aging. , 1999, Cerebral cortex.

[4]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[5]  Osamu Abe,et al.  Human brain structural change related to acute single exposure to sarin , 2007, Annals of neurology.

[6]  Satrajit S. Ghosh,et al.  Instrumentation bias in the use and evaluation of scientific software: recommendations for reproducible practices in the computational sciences , 2013, Front. Neurosci..

[7]  Anders M. Dale,et al.  Cortical Surface-Based Analysis I. Segmentation and Surface Reconstruction , 1999, NeuroImage.

[8]  Knut Engedal,et al.  Frontotemporal Dementia , 2016, Journal of geriatric psychiatry and neurology.

[9]  A. Dale,et al.  Whole Brain Segmentation Automated Labeling of Neuroanatomical Structures in the Human Brain , 2002, Neuron.

[10]  A. Dale,et al.  Regional and progressive thinning of the cortical ribbon in Huntington’s disease , 2002, Neurology.

[11]  Sébastien Ourselin,et al.  A comparison of voxel and surface based cortical thickness estimation methods , 2011, NeuroImage.

[12]  Larson J. Hogstrom,et al.  The structure of the cerebral cortex across adult life: age-related patterns of surface area, thickness, and gyrification. , 2013, Cerebral cortex.

[13]  C. E. Rogers,et al.  Symbolic Description of Factorial Models for Analysis of Variance , 1973 .

[14]  Brian B. Avants,et al.  The optimal template effect in hippocampus studies of diseased populations , 2010, NeuroImage.

[15]  A. M. Dale,et al.  A hybrid approach to the skull stripping problem in MRI , 2004, NeuroImage.

[16]  John A. Detre,et al.  A VBM study demonstrating ‘apparent’ effects of a single dose of medication on T1-weighted MRIs , 2012, Brain Structure and Function.

[17]  Belma Dogdas,et al.  Segmentation of skull and scalp in 3‐D human MRI using mathematical morphology , 2005, Human brain mapping.

[18]  DE Rex,et al.  Gender Effects on Cortical Thickness , 2022 .

[19]  Paul M. Thompson,et al.  Intensity non-uniformity correction using N3 on 3-T scanners with multichannel phased array coils , 2008, NeuroImage.

[20]  T. Jiang,et al.  Increased Cortical Thickness in Sports Experts: A Comparison of Diving Players with the Controls , 2011, PloS one.

[21]  C. Davatzikos,et al.  Using a deformable surface model to obtain a shape representation of the cortex , 1995, Proceedings of International Symposium on Computer Vision - ISCV.

[22]  Alan C. Evans,et al.  A nonparametric method for automatic correction of intensity nonuniformity in MRI data , 1998, IEEE Transactions on Medical Imaging.

[23]  Stephen M. Smith,et al.  Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm , 2001, IEEE Transactions on Medical Imaging.

[24]  B Fischl,et al.  Regional cortical thinning in preclinical Huntington disease and its relationship to cognition , 2005, Neurology.

[25]  Morton Ann Gernsbacher,et al.  Presidential Column: The Eye of the Beholder , 2007 .

[26]  Neil A. Thacker,et al.  A fast, model-independent method for cerebral cortical thickness estimation using MRI , 2009, Medical Image Anal..

[28]  Alan C. Evans,et al.  Automated 3-D Extraction of Inner and Outer Surfaces of Cerebral Cortex from MRI , 2000, NeuroImage.

[29]  Paul A. Yushkevich,et al.  Multi-Atlas Segmentation with Joint Label Fusion , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Satrajit S. Ghosh,et al.  Evaluation of volume-based and surface-based brain image registration methods , 2010, NeuroImage.

[31]  AW Toga,et al.  Brain Structure and Obesity , 2009, NeuroImage.

[32]  Anders M. Fjell,et al.  Blood markers of fatty acids and vitamin D, cardiovascular measures, body mass index, and physical activity relate to longitudinal cortical thinning in normal aging , 2014, Neurobiology of Aging.

[33]  Min Chen,et al.  Multi-parametric neuroimaging reproducibility: A 3-T resource study , 2011, NeuroImage.

[34]  J. Morris,et al.  The Cortical Signature of Alzheimer's Disease: Regionally Specific Cortical Thinning Relates to Symptom Severity in Very Mild to Mild AD Dementia and is Detectable in Asymptomatic Amyloid-Positive Individuals , 2008, Cerebral cortex.

[35]  Martha Elizabeth Shenton,et al.  On evaluating brain tissue classifiers without a ground truth , 2007, NeuroImage.

[36]  D. Collins,et al.  Automatic 3D Intersubject Registration of MR Volumetric Data in Standardized Talairach Space , 1994, Journal of computer assisted tomography.

[37]  R. Leahy,et al.  Magnetic Resonance Image Tissue Classification Using a Partial Volume Model , 2001, NeuroImage.

[38]  Sherif Karama,et al.  BMC Research Notes BioMed Central , 2008 .

[39]  Jelena Kovacevic,et al.  From the editor-in-chief , 2003, IEEE Transactions on Image Processing.

[40]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[41]  D. Neary,et al.  Frontotemporal dementia , 2005, The Lancet Neurology.

[42]  A. Dale,et al.  Cortical Surface-Based Analysis II: Inflation, Flattening, and a Surface-Based Coordinate System , 1999, NeuroImage.

[43]  Alan C. Evans,et al.  Automated 3-D extraction and evaluation of the inner and outer cortical surfaces using a Laplacian map and partial volume effect classification , 2005, NeuroImage.

[44]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[45]  I. Aharon,et al.  Three‐dimensional mapping of cortical thickness using Laplace's Equation , 2000, Human brain mapping.

[46]  Nikos Makris,et al.  Automatically parcellating the human cerebral cortex. , 2004, Cerebral cortex.

[47]  Giovanni B. Frisoni,et al.  Brain morphometry reproducibility in multi-center 3T MRI studies: A comparison of cross-sectional and longitudinal segmentations , 2013, NeuroImage.

[48]  D. Bowers,et al.  Entorhinal cortex volume in older adults: Reliability and validity considerations for three published measurement protocols , 2010, Journal of the International Neuropsychological Society.

[49]  Moo K. Chung,et al.  Cortical thickness analysis in autism with heat kernel smoothing , 2005, NeuroImage.

[50]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[51]  Martin Styner,et al.  Group-wise automatic mesh-based analysis of cortical thickness , 2011, Medical Imaging.

[52]  Arthur W. Toga,et al.  A Probabilistic Atlas of the Human Brain: Theory and Rationale for Its Development The International Consortium for Brain Mapping (ICBM) , 1995, NeuroImage.

[53]  Brian B. Avants,et al.  Registration based cortical thickness measurement , 2009, NeuroImage.

[54]  J. Fleiss,et al.  Intraclass correlations: uses in assessing rater reliability. , 1979, Psychological bulletin.

[55]  Anders M. Dale,et al.  Reliability of MRI-derived measurements of human cerebral cortical thickness: The effects of field strength, scanner upgrade and manufacturer , 2006, NeuroImage.

[56]  Arno Klein,et al.  101 Labeled Brain Images and a Consistent Human Cortical Labeling Protocol , 2012, Front. Neurosci..

[57]  Marianna D. Eddy,et al.  Regionally localized thinning of the cerebral cortex in schizophrenia , 2003, Schizophrenia Research.

[58]  Eileen Luders,et al.  Increased Cortical Thickness in Male-to-Female Transsexualism. , 2012, Journal of behavioral and brain science.

[59]  Bruce Fischl,et al.  Within-subject template estimation for unbiased longitudinal image analysis , 2012, NeuroImage.

[60]  Brian B. Avants,et al.  An Open Source Multivariate Framework for n-Tissue Segmentation with Evaluation on Public Data , 2011, Neuroinformatics.

[61]  Arno Klein,et al.  A reproducible evaluation of ANTs similarity metric performance in brain image registration , 2011, NeuroImage.

[62]  Anders M. Dale,et al.  An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest , 2006, NeuroImage.

[63]  Robert T. Schultz,et al.  Segmentation and Measurement of the Cortex from 3D MR Images , 1998, MICCAI.

[64]  A M Dale,et al.  Measuring the thickness of the human cerebral cortex from magnetic resonance images. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[65]  Brian B. Avants,et al.  N4ITK: Improved N3 Bias Correction , 2010, IEEE Transactions on Medical Imaging.

[66]  Jerry L. Prince,et al.  An Eulerian PDE approach for computing tissue thickness , 2003, IEEE Transactions on Medical Imaging.

[67]  S. M. Smith,et al.  Flexible filter neighbourhood designation , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[68]  N. Schuff,et al.  Different regional patterns of cortical thinning in Alzheimer's disease and frontotemporal dementia. , 2006, Brain : a journal of neurology.

[69]  Alan C. Evans,et al.  Patterns of cortical thickness and surface area in early Parkinson's disease , 2011, NeuroImage.